Optimiertes Design kombinatorischer Verbindungsbibliotheken durch Genetische Algorithmen und deren Bewertung anhand wissensbasierter Protein-Ligand Bindungsprofile

In dieser Arbeit sind die zwei neuen Computer-Methoden DrugScore Fingerprint (DrugScoreFP) und GARLig in ihrer Theorie und Funktionsweise vorgestellt und validiert worden. DrugScoreFP ist ein neuartiger Ansatz zur Bewertung von computergenerierten Bindemodi potentieller Liganden für eine bestimmte Zielstruktur. Das Programm basiert auf der etablierten Bewertungsfunktion DrugScoreCSD und unterscheidet sich darin, dass anhand bereits bekannter Kristallstrukturen für den zu untersuchenden Rezeptor ein Referenzvektor generiert wird, der zu jedem Bindetaschenatom Potentialwerte für alle möglichen Interaktionen enthält. Für jeden neuen, computergenerierten Bindungsmodus eines Liganden lässt sich ein entsprechender Vektor generieren. Dessen Distanz zum Referenzvektor ist ein Maß dafür, wie ähnlich generierte Bindungsmodi zu bereits bekannten sind. Eine experimentelle Validierung der durch DrugScoreFP als ähnlich vorhergesagten Liganden ergab für die in unserem Arbeitskreis untersuchten Proteinstrukturen Trypsin, Thermolysin und tRNA-Guanin Transglykosylase (TGT) sechs Inhibitoren fragmentärer Größe und eine Thermolysin Kristallstruktur in Komplex mit einem der gefundenen Fragmente. Das in dieser Arbeit entwickelte Programm GARLig ist eine auf einem Genetischen Algorithmus basierende Methode, um chemische Seitenkettenmodifikationen niedermolekularer Verbindungen hinsichtlich eines untersuchten Rezeptors effizient durchzuführen. Zielsetzung ist hier die Zusammenstellung einer Verbindungsbibliothek, welche eine benutzerdefiniert große Untermenge aller möglichen chemischen Modifikationen Ligand-ähnlicher Grundgerüste darstellt. Als zentrales Qualitätskriterium einzelner Vertreter der Verbindungsbibliothek dienen durch Docking erzeugte Ligand-Geometrien und deren Bewertungen durch Protein-Ligand-Bewertungsfunktionen. In mehreren Validierungsszenarien an den Proteinen Trypsin, Thrombin, Faktor Xa, Plasmin und Cathepsin D konnte gezeigt werden, dass eine effiziente Zusammenstellung Rezeptor-spezifischer Substrat- oder Ligand-Bibliotheken lediglich eine Durchsuchung von weniger als 8% der vorgegebenen Suchräume erfordert und GARLig dennoch im Stande ist, bekannte Inhibitoren in der Zielbibliothek anzureichern

A Network Pharmacology-Based Study on the Hepatoprotective Effect of Fructus Schisandrae

published_or_final_versio

Integrative Systems Approaches Towards Brain Pharmacology and Polypharmacology

Polypharmacology is considered as the future of drug discovery and emerges as the next paradigm of drug discovery. The traditional drug design is primarily based on a “one target-one drug” paradigm. In polypharmacology, drug molecules always interact with multiple targets, and therefore it imposes new challenges in developing and designing new and effective drugs that are less toxic by eliminating the unexpected drug-target interactions. Although still in its infancy, the use of polypharmacology ideas appears to already have a remarkable impact on modern drug development. The current thesis is a detailed study on various pharmacology approaches at systems level to understand polypharmacology in complex brain and neurodegnerative disorders. The research work in this thesis focuses on the design and construction of a dedicated knowledge base for human brain pharmacology. This pharmacology knowledge base, referred to as the Human Brain Pharmacome (HBP) is a unique and comprehensive resource that aggregates data and knowledge around current drug treatments that are available for major brain and neurodegenerative disorders. The HBP knowledge base provides data at a single place for building models and supporting hypotheses. The HBP also incorporates new data obtained from similarity computations over drugs and proteins structures, which was analyzed from various aspects including network pharmacology and application of in-silico computational methods for the discovery of novel multi-target drug candidates. Computational tools and machine learning models were developed to characterize protein targets for their polypharmacological profiles and to distinguish indications specific or target specific drugs from other drugs. Systems pharmacology approaches towards drug property predictions provided a highly enriched compound library that was virtually screened against an array of network pharmacology based derived protein targets by combined docking and molecular dynamics simulation workflows. The developed approaches in this work resulted in the identification of novel multi-target drug candidates that are backed up by existing experimental knowledge, and propose repositioning of existing drugs, that are undergoing further experimental validations

Targeting The Dimerization Of ERBB Receptor Tyrosine Kinases

The epidermal growth factor receptor: EGFR) is a membrane receptor tyrosine kinase whose over-activation has been implicated to cause many human cancers. Novel strategies to inhibit the activation of EGF receptors other than the conventional antibody-based and tyrosine kinase inhibitors are virtually non-existent but could provide benefits both in the laboratory and clinical settings. In an effort to expand the current approaches, this thesis focused on targeting the homodimerization of the EGF receptors themselves and the heterodimerization of EGF receptors with the related ErbB2 receptor. Three sub-projects were completed in the process. The first project explored the feasibility of inhibiting the EGF receptor by targeting receptor dimerization with small molecules. Two lead compounds were initially predicted by virtual screening the NCI compound library, and were biochemically characterized. The benefit gained from the application of virtual screening in this project initiated another project to enhance the accessibility of virtual screening within the non-computational community. The OpenScreening project utilizes distributed computing resources and provides open-access screening server at: http://omg.phy.umassd.edu/xvhts. A final project identified the structural mechanism that may explain the observed preference of EGFR-ErbB2 heterodimerization over EGFR homodimerization. Key residues were computationally predicted and biochemically tested to reveal critical dimerization interface

Untersuchung der Struktur und Interaktion mit allosterischen Modulatoren der Familie C GPCRs mit Hilfe von Sequenz-, Struktur- und Ligand-basierten Verfahren

This study focuses on structural features of a particular GPCR type, the family C GPCRs. Structure- and ligand-based approaches were adopted for prediction of novel mGluR5 binding ligand and their binding modes. The objectives of this study were: 1. An analysis of function and structural implication of amino acids in the TM region of family C GPCRs. 2. The prediction of the TM domain structure of mGluR5. 3. The discovery of novel selective allosteric modulators of mGluR5 by virtual screening. 4. The prediction of a ligand binding mode for the allosteric binding site in mGluR5. GPCRs are a super-family of structurally related proteins although their primary amino acid sequence can be diverse. Using sequence information a conservation analysis of family C GPCRs should be applied to reveal characteristic differences and similarities with respect function, folding and ligand binding. Using experimental data and conservation analysis the allosteric binding site of mGluR5 should be characterized regarding NAM and PAM and selective ligand binding. For further evaluation experimental knowledge about family A GPCRs as well as conservation between vertebrate rhodopsins was planned to be compared to results obtained for family C GPCRs (Section 4.1 Conservation analysis of family C GPCRs). Since no receptor structure is available for any family C GPCR, discussion of conserved sequence positions between family A and C GPCRs requires the prediction of a receptor structure for mGluR5 using a family A receptor as template. In order to predict the mGluR5 structure a sequence alignment to a GPCR template protein will have to be proposed and GPCR specific features considered in structure calculation (Section 4.1.4 Structure prediction of mGluR5). The obtained structure was intended to be involved in ligand binding mode prediction of newly discovered active molecules. For discovery of novel selective mGluR modulators several ligand-based virtual screening protocols were adapted and evaluated. Prediction models were derived for selection of possibly active molecules using a diverse collection of known mGluR binding ligands. For that purpose a data collection of known mGluR binding ligands should be established and this reference collection analyzed with respect to different ligand activity classes, NAM or PAM and selective modulators. The prediction of novel NAMs and PAMs using several combinations of 2D-, 3D-, pharmacophore or molecule shape encoding methods with machine learning techniques and similarity determining methods should be tested in a prospective manner (Section 4.2 Virtual screening for novel mGluR modulators). In collaboration with Merz Pharmaceuticals (Merz GmbH & Co. KGaA, Frankfurt am Main, Germany) the modulating effect of a few hundred molecules should be approved in a functional cell-based assay. With the objective to predict a binding mode of the discovered active molecules, molecule docking should be applied using the allosteric binding site of the modeled mGluR5 structure (Section 4.2.4 Modeling of binding modes). Predicted ligand binding modes are to be correlated to conservation profiles that had resulted from the sequence-based entropy analysis and information from mutation experiments, and shall be compared to known ligand binding poses from crystal structures of family A GPCRs.Im Rahmen dieser Arbeit wurden Konzepte zur Aufklärung struktureller und funktioneller Eigenschaften von G-Protein gekoppelten Rezeptoren (GPCR) der Familie C entwickelt und angewendet. Mit unterschiedlichen Methodiken der Bio- und Chemieinformatik orientiert an experimentellen Ergebnissen wurden Fragestellungen bezüglich des Funktionsmechanismus von GPCRs untersucht. In Verlauf wurde anhand verfügbarer experimenteller Daten aus Mutations- und Ligandenbindungsstudien ein Vergleich konservierter Bereiche der Rezeptor-Familien A und C angefertigt. Die Konserviertheitsanalyse stützte sich auf die Berechnung der Shannon-Entropie und wurde für ein multiples Sequenzalignment von Transmembrandomänen unterschiedlicher 96 Familie C GPCRs ermittelt. Konservierte Bereiche wurden mit Hilfe experimenteller Daten interpretiert und insbesondere zur Definition von Regionen in der allosterischen Bindetasche hinsichtlich Selektivität verwendet. Mit dem Ziel, neue selektive allosterische Modulatoren für den metabotropen Glutamatrezeptor des Typs fünf (mGluR5) zu finden, wurden mehrere Liganden-basierte Ansätze zur virtuellen Vorhersage der Aktivität von Molekülen entwickelt und getestet. Die dabei angewendete Strategie basierte auf der Kenntnis bereits bekannter Liganden, deren Strukturen und Aktivitätswerte für das Erstellen von Vorhersagemodelle genutzt werden konnten. Die prospektive Vorhersage stützte sich auf unterschiedliche Methoden zur Ähnlichkeitsberechnung und Arten der Molekülkodierung. Die Testung der Moleküle erfolgte hinsichtlich ihrer modulatorischen Wirkung am mGluR5. Die Art der Messung erfasste die Änderungen des Ca2+-Levels in der Zelle. mGluR5-bindende Modulatoren wurden zur Selektivitätsbestimmung einer Testung am mGluR1 unterzogen. Insgesamt konnten 8 von 228 getesteten Molekülen im Aktivitätsbereich unter 10μM ermittelt werden, darunter befand sich ein positiver allosterischer Modulator. Von den restlichen sieben negativen Modulatoren (NAM) waren fünf selektiv für mGluR5. Alle identifizierten NAMs wurden mittels molekularem Dockings auf mögliche Interaktion mit der Transmembrandomäne von mGluR5 untersucht. Die Bindungshypothese entsprach einer Überlagerung der gefundenen Moleküle und ihrer möglicher Interaktionspunkte. Exemplarisch am mGluR5 konnte somit die Eignung einer modellierten GPCR-Struktur für eine Hypothesengenerierung bezüglich Ligandenbindung und struktureller Zusammenhänge untersucht werden

Virtual Screening of Multi-Target Agents by Combinatorial Machine Learning Methods

Ph.DDOCTOR OF PHILOSOPH

De novo drug design through artificial intelligence: an introduction

Developing new drugs is a complex and formidable challenge, intensified by rapidly evolving global health needs. De novo drug design is a promising strategy to accelerate and refine this process. The recent introduction of Generative Artificial Intelligence (AI) algorithms has brought new attention to the field and catalyzed a paradigm shift, allowing rapid and semi-automatic design and optimization of drug-like molecules. This review explores the impact of de novo drug design, highlighting both traditional methodologies and the recently introduced generative algorithms, as well as the promising development of Active Learning (AL). It places special emphasis on their application in oncological drug development, where the need for novel therapeutic agents is urgent. The potential integration of these AI technologies with established computational and experimental methods heralds a new era in the rapid development of innovative drugs. Despite the promising developments and notable successes, these technologies are not without limitations, which require careful consideration and further advancement. This review, intended for professionals across related disciplines, provides a comprehensive introduction to AI-driven de novo drug design of small organic molecules. It aims to offer a clear understanding of the current state and future prospects of these innovative techniques in drug discovery

High performance computational virtual screening tools: development and application to the discovery of kinase inhibitors

Ph.DDOCTOR OF PHILOSOPH

Development of Virtual Screening and In Silico Biomarker Identification Model for Pharmaceutical Agents

Ph.DDOCTOR OF PHILOSOPH

Theoretical-experimental study on protein-ligand interactions based on thermodynamics methods, molecular docking and perturbation models

The current doctoral thesis focuses on understanding the thermodynamic events of protein-ligand interactions which have been of paramount importance from traditional Medicinal Chemistry to Nanobiotechnology. Particular attention has been made on the application of state-of-the-art methodologies to address thermodynamic studies of the protein-ligand interactions by integrating structure-based molecular docking techniques, classical fractal approaches to solve protein-ligand complementarity problems, perturbation models to study allosteric signal propagation, predictive nano-quantitative structure-toxicity relationship models coupled with powerful experimental validation techniques. The contributions provided by this work could open an unlimited horizon to the fields of Drug-Discovery, Materials Sciences, Molecular Diagnosis, and Environmental Health Sciences